TravCav/AdaBoost
Adaptive Boost Algorithm
This algorithm helps you make predictions based on historical data with clear outcomes. You input a list of past situations, each with various characteristics and their eventual result (e.g., 'Discharged' or 'Admitted'). It then uses this information to predict the most likely outcome for new, similar situations. This is useful for data analysts, operations managers, or anyone needing to classify new cases based on past examples.
No commits in the last 6 months. Available on npm.
Use this if you have clear, labeled examples of past events and want to predict the binary outcome (one of two possibilities) for new, similar events.
Not ideal if your data is numerical, your outcomes have more than two categories, or you need to predict a continuous value rather than a classification.
Stars
7
Forks
4
Language
JavaScript
License
MIT
Category
Last pushed
Feb 09, 2018
Commits (30d)
0
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